Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations10076
Missing cells42729
Missing cells (%)28.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.5 MiB
Average record size in memory1.5 KiB

Variable types

Text9
DateTime1
Categorical2
Unsupported2
Numeric1

Alerts

Estado is highly imbalanced (64.1%) Imbalance
Nombre has 1032 (10.2%) missing values Missing
TelefonicoContacto has 213 (2.1%) missing values Missing
TelefonicoAlternativo has 10076 (100.0%) missing values Missing
DomEspecialElectronico has 10076 (100.0%) missing values Missing
Constitucion has 3770 (37.4%) missing values Missing
NumeroEnte has 153 (1.5%) missing values Missing
CorreoInstitucional has 3994 (39.6%) missing values Missing
RegistroPublicoComercio has 7071 (70.2%) missing values Missing
InspeccionGeneralJusticia has 5849 (58.0%) missing values Missing
CUIT has unique values Unique
TelefonicoAlternativo is an unsupported type, check if it needs cleaning or further analysis Unsupported
DomEspecialElectronico is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-03-24 00:25:26.521954
Analysis finished2025-03-24 00:27:00.918969
Duration1 minute and 34.4 seconds
Software versionydata-profiling vv4.15.1
Download configurationconfig.json

Variables

CUIT
Text

Unique 

Distinct10076
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size668.9 KiB
2025-03-23T21:27:01.749067image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length20
Median length11
Mean length10.968936
Min length3

Characters and Unicode

Total characters110523
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10076 ?
Unique (%)100.0%

Sample

1st row27236909900
2nd row30569211685
3rd row30711500363
4th row30708415487
5th row33712286089
ValueCountFrequency (%)
30708516852 1
 
< 0.1%
27331126530 1
 
< 0.1%
27236909900 1
 
< 0.1%
30569211685 1
 
< 0.1%
30711500363 1
 
< 0.1%
30708415487 1
 
< 0.1%
33712286089 1
 
< 0.1%
30583184305 1
 
< 0.1%
30708538317 1
 
< 0.1%
30521417311 1
 
< 0.1%
Other values (10066) 10066
99.9%
2025-03-23T21:27:02.556407image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 17633
16.0%
3 14944
13.5%
2 12785
11.6%
7 12346
11.2%
1 11377
10.3%
6 8863
8.0%
5 8370
7.6%
9 8322
7.5%
4 7960
7.2%
8 7692
7.0%
Other values (28) 231
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 110523
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 17633
16.0%
3 14944
13.5%
2 12785
11.6%
7 12346
11.2%
1 11377
10.3%
6 8863
8.0%
5 8370
7.6%
9 8322
7.5%
4 7960
7.2%
8 7692
7.0%
Other values (28) 231
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 110523
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 17633
16.0%
3 14944
13.5%
2 12785
11.6%
7 12346
11.2%
1 11377
10.3%
6 8863
8.0%
5 8370
7.6%
9 8322
7.5%
4 7960
7.2%
8 7692
7.0%
Other values (28) 231
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 110523
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 17633
16.0%
3 14944
13.5%
2 12785
11.6%
7 12346
11.2%
1 11377
10.3%
6 8863
8.0%
5 8370
7.6%
9 8322
7.5%
4 7960
7.2%
8 7692
7.0%
Other values (28) 231
 
0.2%

Nombre
Text

Missing 

Distinct9039
Distinct (%)99.9%
Missing1032
Missing (%)10.2%
Memory size747.2 KiB
2025-03-23T21:27:03.478854image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length135
Median length92
Mean length20.330385
Min length1

Characters and Unicode

Total characters183868
Distinct characters107
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9034 ?
Unique (%)99.9%

Sample

1st rowEMR VENTAS & SERVICIOS
2nd rowElectricidad Chiclana de R. Santoianni y O.S. Rodriguez
3rd rowLICICOM S.R.L.
4th rowYLUM S.A.
5th rowCOMPAÑÍA DE HIGIENE
ValueCountFrequency (%)
s.a 1772
 
6.4%
srl 1446
 
5.2%
de 899
 
3.2%
sa 842
 
3.0%
s.r.l 803
 
2.9%
y 478
 
1.7%
argentina 325
 
1.2%
servicios 265
 
1.0%
la 223
 
0.8%
cooperativa 170
 
0.6%
Other values (9966) 20549
74.0%
2025-03-23T21:27:05.817829image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18729
 
10.2%
A 14906
 
8.1%
S 11751
 
6.4%
R 9756
 
5.3%
E 9456
 
5.1%
I 9134
 
5.0%
O 7799
 
4.2%
L 7195
 
3.9%
. 6744
 
3.7%
N 6317
 
3.4%
Other values (97) 82081
44.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 183868
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18729
 
10.2%
A 14906
 
8.1%
S 11751
 
6.4%
R 9756
 
5.3%
E 9456
 
5.1%
I 9134
 
5.0%
O 7799
 
4.2%
L 7195
 
3.9%
. 6744
 
3.7%
N 6317
 
3.4%
Other values (97) 82081
44.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 183868
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18729
 
10.2%
A 14906
 
8.1%
S 11751
 
6.4%
R 9756
 
5.3%
E 9456
 
5.1%
I 9134
 
5.0%
O 7799
 
4.2%
L 7195
 
3.9%
. 6744
 
3.7%
N 6317
 
3.4%
Other values (97) 82081
44.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 183868
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18729
 
10.2%
A 14906
 
8.1%
S 11751
 
6.4%
R 9756
 
5.3%
E 9456
 
5.1%
I 9134
 
5.0%
O 7799
 
4.2%
L 7195
 
3.9%
. 6744
 
3.7%
N 6317
 
3.4%
Other values (97) 82081
44.6%
Distinct1803
Distinct (%)18.1%
Missing99
Missing (%)1.0%
Memory size78.8 KiB
Minimum2016-01-08 00:00:00
Maximum2023-06-03 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-23T21:27:06.174189image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-23T21:27:06.717112image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Estado
Categorical

Imbalance 

Distinct10
Distinct (%)0.1%
Missing99
Missing (%)1.0%
Memory size686.0 KiB
Inscripto
7782 
Pre Inscripto
864 
Desactualizado Por Documentos Vencidos
833 
Desactualizado Por Mantencion Formulario
 
254
Desactualizado Por Clase
 
112
Other values (5)
 
132

Length

Max length40
Median length9
Mean length12.841034
Min length9

Characters and Unicode

Total characters128115
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowInscripto
2nd rowDesactualizado Por Documentos Vencidos
3rd rowInscripto
4th rowInscripto
5th rowInscripto

Common Values

ValueCountFrequency (%)
Inscripto 7782
77.2%
Pre Inscripto 864
 
8.6%
Desactualizado Por Documentos Vencidos 833
 
8.3%
Desactualizado Por Mantencion Formulario 254
 
2.5%
Desactualizado Por Clase 112
 
1.1%
Con Solicitud De Baja 81
 
0.8%
En Evaluacion 43
 
0.4%
Suspendido 6
 
0.1%
Inhabilitado 1
 
< 0.1%
Dar De Baja 1
 
< 0.1%
(Missing) 99
 
1.0%

Length

2025-03-23T21:27:07.364533image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T21:27:07.655999image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
inscripto 8646
59.2%
desactualizado 1199
 
8.2%
por 1199
 
8.2%
pre 864
 
5.9%
documentos 833
 
5.7%
vencidos 833
 
5.7%
mantencion 254
 
1.7%
formulario 254
 
1.7%
clase 112
 
0.8%
de 82
 
0.6%
Other values (8) 338
 
2.3%

Most occurring characters

ValueCountFrequency (%)
o 14517
11.3%
c 11889
9.3%
s 11629
9.1%
i 11399
8.9%
n 11248
8.8%
r 11218
8.8%
t 11014
8.6%
p 8652
 
6.8%
I 8647
 
6.7%
4637
 
3.6%
Other values (20) 23265
18.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 128115
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 14517
11.3%
c 11889
9.3%
s 11629
9.1%
i 11399
8.9%
n 11248
8.8%
r 11218
8.8%
t 11014
8.6%
p 8652
 
6.8%
I 8647
 
6.7%
4637
 
3.6%
Other values (20) 23265
18.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 128115
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 14517
11.3%
c 11889
9.3%
s 11629
9.1%
i 11399
8.9%
n 11248
8.8%
r 11218
8.8%
t 11014
8.6%
p 8652
 
6.8%
I 8647
 
6.7%
4637
 
3.6%
Other values (20) 23265
18.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 128115
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 14517
11.3%
c 11889
9.3%
s 11629
9.1%
i 11399
8.9%
n 11248
8.8%
r 11218
8.8%
t 11014
8.6%
p 8652
 
6.8%
I 8647
 
6.7%
4637
 
3.6%
Other values (20) 23265
18.2%

TelefonicoContacto
Text

Missing 

Distinct9654
Distinct (%)97.9%
Missing213
Missing (%)2.1%
Memory size661.2 KiB
2025-03-23T21:27:08.604828image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length20
Median length18
Mean length10.933387
Min length1

Characters and Unicode

Total characters107836
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9530 ?
Unique (%)96.6%

Sample

1st row2804357488
2nd row54114923-4922
3rd row45833433
4th row541149116641
5th row541147901907
ValueCountFrequency (%)
50719546 46
 
0.5%
11 27
 
0.3%
54 23
 
0.2%
9 13
 
0.1%
50719386 8
 
0.1%
011 7
 
0.1%
1 7
 
0.1%
42378281 6
 
0.1%
48078081 6
 
0.1%
0351 5
 
< 0.1%
Other values (9716) 9874
98.5%
2025-03-23T21:27:09.658727image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 16417
15.2%
1 15968
14.8%
5 13125
12.2%
0 11680
10.8%
2 10687
9.9%
3 10069
9.3%
6 7672
7.1%
9 6978
6.5%
7 6885
6.4%
8 6324
 
5.9%
Other values (15) 2031
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 107836
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 16417
15.2%
1 15968
14.8%
5 13125
12.2%
0 11680
10.8%
2 10687
9.9%
3 10069
9.3%
6 7672
7.1%
9 6978
6.5%
7 6885
6.4%
8 6324
 
5.9%
Other values (15) 2031
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 107836
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 16417
15.2%
1 15968
14.8%
5 13125
12.2%
0 11680
10.8%
2 10687
9.9%
3 10069
9.3%
6 7672
7.1%
9 6978
6.5%
7 6885
6.4%
8 6324
 
5.9%
Other values (15) 2031
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 107836
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 16417
15.2%
1 15968
14.8%
5 13125
12.2%
0 11680
10.8%
2 10687
9.9%
3 10069
9.3%
6 7672
7.1%
9 6978
6.5%
7 6885
6.4%
8 6324
 
5.9%
Other values (15) 2031
 
1.9%

TelefonicoAlternativo
Unsupported

Missing  Rejected  Unsupported 

Missing10076
Missing (%)100.0%
Memory size78.8 KiB

DomEspecialElectronico
Unsupported

Missing  Rejected  Unsupported 

Missing10076
Missing (%)100.0%
Memory size78.8 KiB

Constitucion
Text

Missing 

Distinct6214
Distinct (%)98.5%
Missing3770
Missing (%)37.4%
Memory size783.3 KiB
2025-03-23T21:27:10.670336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length74
Median length71
Mean length40.473834
Min length23

Characters and Unicode

Total characters255228
Distinct characters92
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6133 ?
Unique (%)97.3%

Sample

1st rowEn C.A.B.A. con fecha 28/05/1980
2nd rowEn JUAN B JUSTO Nº 925, LANUS, PROV DE BS AS con fecha 22/06/2010
3rd rowEn Rodriguez Peña 694 5º D, CABA con fecha 28/03/2003
4th rowEn BUENOS AIRES con fecha 08/07/2011
5th rowEn BUENOS AIRES con fecha 25/08/1980
ValueCountFrequency (%)
en 6311
14.8%
fecha 6308
14.8%
con 6307
14.8%
buenos 2581
 
6.1%
aires 2576
 
6.0%
de 2136
 
5.0%
ciudad 1772
 
4.2%
autonoma 718
 
1.7%
autónoma 610
 
1.4%
caba 601
 
1.4%
Other values (5730) 12715
29.8%
2025-03-23T21:27:11.837170image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36329
 
14.2%
n 16492
 
6.5%
0 14428
 
5.7%
c 13208
 
5.2%
e 12714
 
5.0%
/ 12619
 
4.9%
a 12162
 
4.8%
o 10711
 
4.2%
1 10529
 
4.1%
E 9922
 
3.9%
Other values (82) 106114
41.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 255228
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
36329
 
14.2%
n 16492
 
6.5%
0 14428
 
5.7%
c 13208
 
5.2%
e 12714
 
5.0%
/ 12619
 
4.9%
a 12162
 
4.8%
o 10711
 
4.2%
1 10529
 
4.1%
E 9922
 
3.9%
Other values (82) 106114
41.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 255228
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
36329
 
14.2%
n 16492
 
6.5%
0 14428
 
5.7%
c 13208
 
5.2%
e 12714
 
5.0%
/ 12619
 
4.9%
a 12162
 
4.8%
o 10711
 
4.2%
1 10529
 
4.1%
E 9922
 
3.9%
Other values (82) 106114
41.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 255228
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
36329
 
14.2%
n 16492
 
6.5%
0 14428
 
5.7%
c 13208
 
5.2%
e 12714
 
5.0%
/ 12619
 
4.9%
a 12162
 
4.8%
o 10711
 
4.2%
1 10529
 
4.1%
E 9922
 
3.9%
Other values (82) 106114
41.6%

TipoSocietario
Categorical

Distinct11
Distinct (%)0.1%
Missing99
Missing (%)1.0%
Memory size1.0 MiB
Persona Física
3647 
Sociedad Anónima
2845 
Sociedad Responsabilidad Limitada
2342 
Otras Formas Societarias
 
344
Persona Jurídica Extranjero Sin Sucursal
 
256
Other values (6)
543 

Length

Max length40
Median length38
Mean length20.20427
Min length12

Characters and Unicode

Total characters201578
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPersona Física
2nd rowSociedades De Hecho
3rd rowSociedad Responsabilidad Limitada
4th rowSociedad Anónima
5th rowSociedad Responsabilidad Limitada

Common Values

ValueCountFrequency (%)
Persona Física 3647
36.2%
Sociedad Anónima 2845
28.2%
Sociedad Responsabilidad Limitada 2342
23.2%
Otras Formas Societarias 344
 
3.4%
Persona Jurídica Extranjero Sin Sucursal 256
 
2.5%
Organismo Publico 200
 
2.0%
Cooperativas 187
 
1.9%
Sociedades De Hecho 117
 
1.2%
Persona Física Extranjero No Residente 24
 
0.2%
Unión Transitoria de Empresas 14
 
0.1%
(Missing) 99
 
1.0%

Length

2025-03-23T21:27:12.086798image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sociedad 5187
22.1%
persona 3927
16.8%
física 3671
15.7%
anónima 2845
12.1%
responsabilidad 2342
10.0%
limitada 2342
10.0%
otras 344
 
1.5%
formas 344
 
1.5%
societarias 344
 
1.5%
extranjero 280
 
1.2%
Other values (17) 1814
 
7.7%

Most occurring characters

ValueCountFrequency (%)
a 27900
13.8%
i 23043
11.4%
d 17931
 
8.9%
s 14142
 
7.0%
o 13473
 
6.7%
13463
 
6.7%
e 12839
 
6.4%
n 12762
 
6.3%
c 10150
 
5.0%
r 6463
 
3.2%
Other values (28) 49412
24.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 201578
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 27900
13.8%
i 23043
11.4%
d 17931
 
8.9%
s 14142
 
7.0%
o 13473
 
6.7%
13463
 
6.7%
e 12839
 
6.4%
n 12762
 
6.3%
c 10150
 
5.0%
r 6463
 
3.2%
Other values (28) 49412
24.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 201578
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 27900
13.8%
i 23043
11.4%
d 17931
 
8.9%
s 14142
 
7.0%
o 13473
 
6.7%
13463
 
6.7%
e 12839
 
6.4%
n 12762
 
6.3%
c 10150
 
5.0%
r 6463
 
3.2%
Other values (28) 49412
24.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 201578
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 27900
13.8%
i 23043
11.4%
d 17931
 
8.9%
s 14142
 
7.0%
o 13473
 
6.7%
13463
 
6.7%
e 12839
 
6.4%
n 12762
 
6.3%
c 10150
 
5.0%
r 6463
 
3.2%
Other values (28) 49412
24.5%

NumeroEnte
Real number (ℝ)

Missing 

Distinct9922
Distinct (%)> 99.9%
Missing153
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean467231.33
Minimum103
Maximum1416772
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size88.7 KiB
2025-03-23T21:27:12.258588image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum103
5-th percentile7823.8
Q1208006.5
median479590
Q3647880
95-th percentile1111148.5
Maximum1416772
Range1416669
Interquartile range (IQR)439873.5

Descriptive statistics

Standard deviation315570.87
Coefficient of variation (CV)0.67540606
Kurtosis0.069609007
Mean467231.33
Median Absolute Deviation (MAD)196506
Skewness0.55036037
Sum4.6363364 × 109
Variance9.9584974 × 1010
MonotonicityNot monotonic
2025-03-23T21:27:13.657254image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1241.0 2
 
< 0.1%
3617.0 1
 
< 0.1%
389378.0 1
 
< 0.1%
1284300.0 1
 
< 0.1%
254245.0 1
 
< 0.1%
237357.0 1
 
< 0.1%
1330129.0 1
 
< 0.1%
1326920.0 1
 
< 0.1%
1110000.0 1
 
< 0.1%
108334.0 1
 
< 0.1%
Other values (9912) 9912
98.4%
(Missing) 153
 
1.5%
ValueCountFrequency (%)
103.0 1
< 0.1%
315.0 1
< 0.1%
320.0 1
< 0.1%
321.0 1
< 0.1%
326.0 1
< 0.1%
334.0 1
< 0.1%
372.0 1
< 0.1%
375.0 1
< 0.1%
379.0 1
< 0.1%
380.0 1
< 0.1%
ValueCountFrequency (%)
1416772.0 1
< 0.1%
1409657.0 1
< 0.1%
1409422.0 1
< 0.1%
1408203.0 1
< 0.1%
1405282.0 1
< 0.1%
1393718.0 1
< 0.1%
1392476.0 1
< 0.1%
1391241.0 1
< 0.1%
1391213.0 1
< 0.1%
1390752.0 1
< 0.1%
Distinct9885
Distinct (%)99.1%
Missing99
Missing (%)1.0%
Memory size3.6 MiB
2025-03-23T21:27:14.611257image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length233
Median length196
Mean length145.5222
Min length8

Characters and Unicode

Total characters1451875
Distinct characters104
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9809 ?
Unique (%)98.3%

Sample

1st rowCACIQUE FRANCISCO 1398, localidad TRELEW, departamento RAWSON, provincia Chubut, Argentina, código postal 9100
2nd rowAv. Boedo 1986, piso N° P.B., localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal C1239AAW
3rd rowAV CORRIENTES 4709, piso N° 3, depto N° 36, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1414
4th rowRodriguez Peña 694, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal C1020ADN
5th rowO'Higgins 3550, localidad OLIVOS, departamento VICENTE LOPEZ, provincia Buenos Aires, Argentina, código postal 1636
ValueCountFrequency (%)
buenos 13379
 
6.5%
aires 13379
 
6.5%
de 12542
 
6.1%
ciudad 10978
 
5.4%
autónoma 10918
 
5.3%
localidad 9774
 
4.8%
provincia 9772
 
4.8%
departamento 9771
 
4.8%
código 9770
 
4.8%
postal 9770
 
4.8%
Other values (9094) 94243
46.1%
2025-03-23T21:27:15.661210image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
194320
 
13.4%
a 102930
 
7.1%
o 87758
 
6.0%
i 82519
 
5.7%
d 78591
 
5.4%
e 78354
 
5.4%
n 69197
 
4.8%
A 63920
 
4.4%
t 56952
 
3.9%
, 55769
 
3.8%
Other values (94) 581565
40.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1451875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
194320
 
13.4%
a 102930
 
7.1%
o 87758
 
6.0%
i 82519
 
5.7%
d 78591
 
5.4%
e 78354
 
5.4%
n 69197
 
4.8%
A 63920
 
4.4%
t 56952
 
3.9%
, 55769
 
3.8%
Other values (94) 581565
40.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1451875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
194320
 
13.4%
a 102930
 
7.1%
o 87758
 
6.0%
i 82519
 
5.7%
d 78591
 
5.4%
e 78354
 
5.4%
n 69197
 
4.8%
A 63920
 
4.4%
t 56952
 
3.9%
, 55769
 
3.8%
Other values (94) 581565
40.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1451875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
194320
 
13.4%
a 102930
 
7.1%
o 87758
 
6.0%
i 82519
 
5.7%
d 78591
 
5.4%
e 78354
 
5.4%
n 69197
 
4.8%
A 63920
 
4.4%
t 56952
 
3.9%
, 55769
 
3.8%
Other values (94) 581565
40.1%
Distinct9851
Distinct (%)98.7%
Missing99
Missing (%)1.0%
Memory size3.5 MiB
2025-03-23T21:27:16.379755image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length233
Median length198
Mean length144.03648
Min length6

Characters and Unicode

Total characters1437052
Distinct characters103
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9768 ?
Unique (%)97.9%

Sample

1st rowCACIQUE FRANCISCO 1398, localidad TRELEW, departamento RAWSON, provincia Chubut, Argentina, código postal 9100
2nd rowAv. Boedo 1986, piso N° P.B., localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal C1239AAW
3rd rowSAN BLAS 2257, piso N° PB, depto N° PB, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1416
4th rowFamatina 3933, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal C1437IOU
5th rowObispo San Alberto 2975, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1419
ValueCountFrequency (%)
aires 13099
 
6.5%
buenos 13098
 
6.5%
de 11968
 
6.0%
ciudad 10371
 
5.2%
autónoma 10307
 
5.1%
localidad 9802
 
4.9%
provincia 9801
 
4.9%
departamento 9800
 
4.9%
código 9760
 
4.9%
postal 9760
 
4.9%
Other values (8989) 93154
46.4%
2025-03-23T21:27:17.382290image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
190943
 
13.3%
a 101638
 
7.1%
o 86191
 
6.0%
i 81279
 
5.7%
e 76809
 
5.3%
d 76450
 
5.3%
n 68395
 
4.8%
A 63807
 
4.4%
t 56056
 
3.9%
, 55253
 
3.8%
Other values (93) 580231
40.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1437052
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
190943
 
13.3%
a 101638
 
7.1%
o 86191
 
6.0%
i 81279
 
5.7%
e 76809
 
5.3%
d 76450
 
5.3%
n 68395
 
4.8%
A 63807
 
4.4%
t 56056
 
3.9%
, 55253
 
3.8%
Other values (93) 580231
40.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1437052
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
190943
 
13.3%
a 101638
 
7.1%
o 86191
 
6.0%
i 81279
 
5.7%
e 76809
 
5.3%
d 76450
 
5.3%
n 68395
 
4.8%
A 63807
 
4.4%
t 56056
 
3.9%
, 55253
 
3.8%
Other values (93) 580231
40.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1437052
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
190943
 
13.3%
a 101638
 
7.1%
o 86191
 
6.0%
i 81279
 
5.7%
e 76809
 
5.3%
d 76450
 
5.3%
n 68395
 
4.8%
A 63807
 
4.4%
t 56056
 
3.9%
, 55253
 
3.8%
Other values (93) 580231
40.4%

CorreoInstitucional
Text

Missing 

Distinct5998
Distinct (%)98.6%
Missing3994
Missing (%)39.6%
Memory size611.3 KiB
2025-03-23T21:27:18.132145image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length48
Median length40
Mean length24.885071
Min length10

Characters and Unicode

Total characters151351
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5929 ?
Unique (%)97.5%

Sample

1st rowelectricidadchiclana@e-chiclana.com.ar
2nd rowLICICOM.SRL@GMAIL.COM
3rd rowylumsa@ylumsa.com.ar
4th rowinfo@companiadehigiene.com.ar
5th rowinfo@orlando-srl.com.ar
ValueCountFrequency (%)
fecootraunfv@hotmail.com 6
 
0.1%
ldurandeu@hotmail.com 5
 
0.1%
ds@fasempresas.com.ar 4
 
0.1%
federacion1demayo@gmail.com 4
 
0.1%
pbollag@wasserberg.com 3
 
< 0.1%
info@wasserberg.com 3
 
< 0.1%
clientesguadalquivir@fibertel.com.ar 3
 
< 0.1%
cra.sufanyamila@gmail.com 3
 
< 0.1%
jlancioni@expresolancioni.com 3
 
< 0.1%
ventas6@jieli.com.ar 2
 
< 0.1%
Other values (5986) 6046
99.4%
2025-03-23T21:27:19.671835image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 16139
 
10.7%
o 14689
 
9.7%
c 10954
 
7.2%
i 10869
 
7.2%
r 10597
 
7.0%
. 10154
 
6.7%
m 9912
 
6.5%
e 8570
 
5.7%
n 7659
 
5.1%
s 6530
 
4.3%
Other values (56) 45278
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 151351
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 16139
 
10.7%
o 14689
 
9.7%
c 10954
 
7.2%
i 10869
 
7.2%
r 10597
 
7.0%
. 10154
 
6.7%
m 9912
 
6.5%
e 8570
 
5.7%
n 7659
 
5.1%
s 6530
 
4.3%
Other values (56) 45278
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 151351
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 16139
 
10.7%
o 14689
 
9.7%
c 10954
 
7.2%
i 10869
 
7.2%
r 10597
 
7.0%
. 10154
 
6.7%
m 9912
 
6.5%
e 8570
 
5.7%
n 7659
 
5.1%
s 6530
 
4.3%
Other values (56) 45278
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 151351
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 16139
 
10.7%
o 14689
 
9.7%
c 10954
 
7.2%
i 10869
 
7.2%
r 10597
 
7.0%
. 10154
 
6.7%
m 9912
 
6.5%
e 8570
 
5.7%
n 7659
 
5.1%
s 6530
 
4.3%
Other values (56) 45278
29.9%
Distinct2841
Distinct (%)94.5%
Missing7071
Missing (%)70.2%
Memory size437.3 KiB
2025-03-23T21:27:21.054268image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length50
Median length47
Mean length10.92213
Min length1

Characters and Unicode

Total characters32821
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2758 ?
Unique (%)91.8%

Sample

1st row3886
2nd row8196
3rd row2509 LIBRO 77 de SRL
4th row15208
5th row11319
ValueCountFrequency (%)
libro 291
 
4.5%
tomo 253
 
3.9%
folio 221
 
3.4%
155
 
2.4%
105
 
1.6%
a 98
 
1.5%
de 93
 
1.4%
srl 49
 
0.8%
45
 
0.7%
0 40
 
0.6%
Other values (3544) 5121
79.1%
2025-03-23T21:27:22.863976image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3466
 
10.6%
1 3011
 
9.2%
2 2032
 
6.2%
0 1786
 
5.4%
3 1696
 
5.2%
5 1628
 
5.0%
4 1540
 
4.7%
9 1489
 
4.5%
6 1447
 
4.4%
8 1424
 
4.3%
Other values (70) 13302
40.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32821
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3466
 
10.6%
1 3011
 
9.2%
2 2032
 
6.2%
0 1786
 
5.4%
3 1696
 
5.2%
5 1628
 
5.0%
4 1540
 
4.7%
9 1489
 
4.5%
6 1447
 
4.4%
8 1424
 
4.3%
Other values (70) 13302
40.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32821
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3466
 
10.6%
1 3011
 
9.2%
2 2032
 
6.2%
0 1786
 
5.4%
3 1696
 
5.2%
5 1628
 
5.0%
4 1540
 
4.7%
9 1489
 
4.5%
6 1447
 
4.4%
8 1424
 
4.3%
Other values (70) 13302
40.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32821
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3466
 
10.6%
1 3011
 
9.2%
2 2032
 
6.2%
0 1786
 
5.4%
3 1696
 
5.2%
5 1628
 
5.0%
4 1540
 
4.7%
9 1489
 
4.5%
6 1447
 
4.4%
8 1424
 
4.3%
Other values (70) 13302
40.5%
Distinct4092
Distinct (%)96.8%
Missing5849
Missing (%)58.0%
Memory size465.1 KiB
2025-03-23T21:27:23.772752image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length50
Median length48
Mean length9.2493494
Min length1

Characters and Unicode

Total characters39097
Distinct characters85
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4038 ?
Unique (%)95.5%

Sample

1st row53538
2nd row1718555
3rd row1846001
4th row1729875
5th row4336 LIBRO 79 SRL
ValueCountFrequency (%)
libro 429
 
6.1%
tomo 245
 
3.5%
de 172
 
2.4%
a 128
 
1.8%
102
 
1.4%
srl 102
 
1.4%
del 54
 
0.8%
folio 52
 
0.7%
48
 
0.7%
sa 47
 
0.7%
Other values (4516) 5674
80.4%
2025-03-23T21:27:25.118763image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4794
 
12.3%
2826
 
7.2%
0 2546
 
6.5%
2 2532
 
6.5%
7 2428
 
6.2%
8 2314
 
5.9%
9 2248
 
5.7%
6 2204
 
5.6%
5 2192
 
5.6%
3 2131
 
5.5%
Other values (75) 12882
32.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39097
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4794
 
12.3%
2826
 
7.2%
0 2546
 
6.5%
2 2532
 
6.5%
7 2428
 
6.2%
8 2314
 
5.9%
9 2248
 
5.7%
6 2204
 
5.6%
5 2192
 
5.6%
3 2131
 
5.5%
Other values (75) 12882
32.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39097
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4794
 
12.3%
2826
 
7.2%
0 2546
 
6.5%
2 2532
 
6.5%
7 2428
 
6.2%
8 2314
 
5.9%
9 2248
 
5.7%
6 2204
 
5.6%
5 2192
 
5.6%
3 2131
 
5.5%
Other values (75) 12882
32.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39097
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4794
 
12.3%
2826
 
7.2%
0 2546
 
6.5%
2 2532
 
6.5%
7 2428
 
6.2%
8 2314
 
5.9%
9 2248
 
5.7%
6 2204
 
5.6%
5 2192
 
5.6%
3 2131
 
5.5%
Other values (75) 12882
32.9%

Interactions

2025-03-23T21:25:30.690398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-03-23T21:27:25.348925image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
EstadoNumeroEnteTipoSocietario
Estado1.0000.0300.190
NumeroEnte0.0301.0000.025
TipoSocietario0.1900.0251.000

Missing values

2025-03-23T21:26:57.645343image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-23T21:26:58.530320image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-23T21:26:59.736203image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CUITNombreFechaPreinscripcionEstadoTelefonicoContactoTelefonicoAlternativoDomEspecialElectronicoConstitucionTipoSocietarioNumeroEnteDomicilioLegalDomicilioEspecialCorreoInstitucionalRegistroPublicoComercioInspeccionGeneralJusticia
027236909900EMR VENTAS & SERVICIOS04/10/2016Inscripto2804357488NaNNaNNaNPersona Física433508.0CACIQUE FRANCISCO 1398, localidad TRELEW, departamento RAWSON, provincia Chubut, Argentina, código postal 9100CACIQUE FRANCISCO 1398, localidad TRELEW, departamento RAWSON, provincia Chubut, Argentina, código postal 9100NaNNaNNaN
130569211685Electricidad Chiclana de R. Santoianni y O.S. Rodriguez18/08/2016Desactualizado Por Documentos Vencidos54114923-4922NaNNaNEn C.A.B.A. con fecha 28/05/1980Sociedades De Hecho3617.0Av. Boedo 1986, piso N° P.B., localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal C1239AAWAv. Boedo 1986, piso N° P.B., localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal C1239AAWelectricidadchiclana@e-chiclana.com.arNaNNaN
230711500363LICICOM S.R.L.15/09/2016Inscripto45833433NaNNaNEn JUAN B JUSTO Nº 925, LANUS, PROV DE BS AS con fecha 22/06/2010Sociedad Responsabilidad Limitada389378.0AV CORRIENTES 4709, piso N° 3, depto N° 36, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1414SAN BLAS 2257, piso N° PB, depto N° PB, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1416LICICOM.SRL@GMAIL.COMNaN53538
330708415487YLUM S.A.24/08/2016Inscripto541149116641NaNNaNEn Rodriguez Peña 694 5º D, CABA con fecha 28/03/2003Sociedad Anónima214190.0Rodriguez Peña 694, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal C1020ADNFamatina 3933, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal C1437IOUylumsa@ylumsa.com.ar38861718555
433712286089COMPAÑÍA DE HIGIENE26/10/2016Inscripto541147901907NaNNaNEn BUENOS AIRES con fecha 08/07/2011Sociedad Responsabilidad Limitada471565.0O'Higgins 3550, localidad OLIVOS, departamento VICENTE LOPEZ, provincia Buenos Aires, Argentina, código postal 1636Obispo San Alberto 2975, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1419info@companiadehigiene.com.ar81961846001
530583184305MATAFUEGOS ORLANDO S.R.L.02/11/2016Desactualizado Por Documentos Vencidos541146726278NaNNaNEn BUENOS AIRES con fecha 25/08/1980Sociedad Responsabilidad Limitada1809.0JUAN F. ARANGUREN 4289, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal C1407ELSJUAN F ARANGUREN 4289, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal C1407ELSinfo@orlando-srl.com.ar2509 LIBRO 77 de SRLNaN
630708538317CAROLS SA09/09/2016Inscripto43019105NaNNaNEn Ciudad Autonoma de Buenos Aires * Cap fed con fecha 03/11/2003Sociedad Anónima197137.0Bto.Quinquela Martin 2150, piso N° **, depto N° **, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1296Bto Quinquela Martin 2150, piso N° **, depto N° **, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1296INFO@CAROLS.COM.AR152081729875
730521417311CONFECCIONES JOSE CONTARTESE Y CIA S.R.L.12/09/2016Inscripto0541146534296NaNNaNEn CIUDAD AUTONOMA DE BUENOS AIRES con fecha 31/10/1980Sociedad Responsabilidad Limitada1671.0COSTA RICA 5978, piso N° P. BAJA, depto N° 2, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal C1414BTLCOSTA RICA 5978, piso N° P. BAJA, depto N° 2, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal C1414BTLinfo@contartese.com.arNaN4336 LIBRO 79 SRL
820305924076Suministros EDA13/10/2016Inscripto45744186NaNNaNNaNPersona Física483729.0HORTIGUERA 114, piso N° 1º, depto N° B, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1406HORTIGUERA 114, piso N° 1º, depto N° B, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1406NaNNaNNaN
920082883240SEGUMAX de HORACIO MIGUEL ESPOSITO18/10/2016Desactualizado Por Documentos Vencidos0114831-1535NaNNaNNaNPersona Física133832.0AYACUCHO 2175, piso N° 3°, depto N° B, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1112ARAOZ 1327, piso N° 2, depto N° A, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1414NaNNaNNaN
CUITNombreFechaPreinscripcionEstadoTelefonicoContactoTelefonicoAlternativoDomEspecialElectronicoConstitucionTipoSocietarioNumeroEnteDomicilioLegalDomicilioEspecialCorreoInstitucionalRegistroPublicoComercioInspeccionGeneralJusticia
1006630716032503BIOPAZ S.A.11/06/2021Inscripto3794267234NaNNaNEn Corrientes, Capital con fecha 08/05/2018Sociedad Anónima1110000.0RUTA 12 1049, localidad SANTA ANA, departamento SAN COSME, provincia Corrientes, Argentina, código postal 3400Pellegrini 1029, piso N° PA, localidad CORRIENTES, departamento CAPITAL, provincia Corrientes, Argentina, código postal 3400biopazsrl@gmail.com01171, Libro VII, Tomo INaN
1006720171591563BIOTECNIKA16/06/2022Inscripto3515553228NaNNaNNaNPersona Física108334.0Avellaneda 140, piso N° 1, depto N° 9, localidad TEMPERLEY, departamento LOMAS DE ZAMORA, provincia Buenos Aires, Argentina, código postal 1834pasaje Uehara 100, localidad UNQUILLO, departamento COLON, provincia Córdoba, Argentina, código postal 5109NaNNaNNaN
1006820293290416ELIAS MARTIN SEGURA26/08/2022Inscripto1166048064NaNNaNNaNPersona Física1327602.0ZAMUDIO 5027, localidad CABA, departamento CABA, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1419ZAMUDIO 5027, localidad CABA, departamento CABA, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1419NaNNaNNaN
1006920240423759FEDERICO MARTIN NUÑEZ26/08/2022Inscripto1162520348NaNNaNNaNPersona Física1321848.0GODOY CRUZ 2449, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1425GODOY CRUZ 2449, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1425NaNNaNNaN
1007030710308051ZENSEI SRL30/05/2017Inscripto1152914747NaNNaNEn Ciudad Autonoma de Buenos Aires con fecha 27/08/2007Sociedad Anónima1884.0concepcion arenal 2978, piso N° pb , depto N° h, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1426concepcion arenal 2978, piso N° pb, depto N° h, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1426administracion@zensei.com.arNaN8175 del libro 127 tomo SRL
1007120287286687LAZARTE MARIO03/08/2022Inscripto2235465989NaNNaNNaNPersona Física3333.0CARAZA 3530, localidad MAR DEL PLATA, departamento GENERAL PUEYRREDON, provincia Buenos Aires, Argentina, código postal 7600CARAZA 3530, localidad MAR DEL PLATA, departamento GENERAL PUEYRREDON, provincia Buenos Aires, Argentina, código postal 7600NaNNaNNaN
1007230518773743Hotel Astor Sociedad Anonima Comercial25/07/2022Inscripto541143222400NaNNaNEn Tigre con fecha 09/12/1968Sociedad Anónima1622.0Azcuenaga 1721, piso N° 6, depto N° G, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1128Entre Rios 1649, localidad MAR DEL PLATA, departamento GENERAL PUEYRREDON, provincia Buenos Aires, Argentina, código postal 7600administracion@comercialdeturismo.comNaN478847
1007330700503891GIJON SA07/09/2022Inscripto1160190901NaNNaNEn CABA con fecha 23/07/1998Sociedad Anónima1850.0VERA PEÑALOZA,ROSARIO BOULEVARD 360, localidad CABA, departamento CABA, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1107VERA PEÑALOZA,ROSARIO BOULEVARD 360, localidad CABA, departamento CABA, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1107jdiaz@hotelmadero.comNaN20959
1007430716441098LISTOS PARA RODAR SAS16/08/2019Inscripto1531111101NaNNaNEn BUENOS AIRES con fecha 01/04/2019Otras Formas Societarias6782.0CRAMER 40, piso N° 1, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1426CRAMER 40, piso N° 1, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1426info@listospararodar.com.ar3071644109830716441098
1007527331126530NaN18/08/2022Inscripto1167418098NaNNaNNaNPersona Física8055.0Salcedo 3588, piso N° 1, depto N° C, localidad CABA, departamento CABA, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1259Salcedo 3588, piso N° 1, depto N° C, localidad CABA, departamento CABA, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1259NaNNaNNaN